Dataset Information

MicroRNAs in cardiovascular disease: from pathogenesis to prevention and treatment.

ABSTRACT: The management of cardiovascular risk through lifestyle modification and pharmacotherapy is paramount to the prevention of cardiovascular disease. Epidemiological studies have identified obesity, dyslipidemia, diabetes, and hypertension as interrelated factors that negatively affect cardiovascular health. Recently, genetic and pharmacological evidence in model systems has implicated microRNAs as dynamic modifiers of disease pathogenesis. An expanded understanding of the function of microRNAs in gene regulatory networks associated with cardiovascular risk will enable identification of novel genetic mechanisms of disease and inform the development of innovative therapeutic strategies.

SUBMITTER: Quiat D

PROVIDER: S-EPMC3533276 | biostudies-other | 2013 Jan

REPOSITORIES: biostudies-other

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Publications

MicroRNAs in cardiovascular disease: from pathogenesis to prevention and treatment.

Quiat Daniel D Olson Eric N EN

The Journal of clinical investigation 20130102 1

The management of cardiovascular risk through lifestyle modification and pharmacotherapy is paramount to the prevention of cardiovascular disease. Epidemiological studies have identified obesity, dyslipidemia, diabetes, and hypertension as interrelated factors that negatively affect cardiovascular health. Recently, genetic and pharmacological evidence in model systems has implicated microRNAs as dynamic modifiers of disease pathogenesis. An expanded understanding of the function of microRNAs in ...[more]

PMID: 23281405

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Project description:Statin therapy is the cornerstone of preventing atherosclerotic cardiovascular disease (ASCVD), primarily by reducing low density lipoprotein cholesterol (LDL-C) levels. Optimal statin therapy decisions rely on shared decision making and may be uncertain for a given patient. In areas of clinical uncertainty, personalized approaches based on real-world data may help inform treatment decisions. We sought to develop a personalized statin recommendation approach for primary ASCVD prevention based on historical real-world outcomes in similar patients. Our retrospective cohort included adults from a large Northern California electronic health record (EHR) aged 40-79 years with no prior cardiovascular disease or statin use. The cohort was split into training and test sets. Weighted-K-nearest-neighbor (wKNN) regression models were used to identify historical EHR patients similar to a candidate patient. We modeled four statin decisions for each patient: none, low-intensity, moderate-intensity, and high-intensity. For each candidate patient, the algorithm recommended the statin decision that was associated with the greatest percentage reduction in LDL-C after 1 year in similar patients. The overall cohort consisted of 50,576 patients (age 54.6 ± 9.8 years) with 55% female, 48% non-Hispanic White, 32% Asian, and 7.4% Hispanic patients. Among 8383 test-set patients, 52%, 44%, and 4% were recommended high-, moderate-, and low-intensity statins, respectively, for a maximum predicted average 1-yr LDL-C reduction of 16.9%, 20.4%, and 14.9%, in each group, respectively. Overall, using aggregate EHR data, a personalized statin recommendation approach identified the statin intensity associated with the greatest LDL-C reduction in historical patients similar to a candidate patient. Recommendations included low- or moderate-intensity statins for maximum LDL-C lowering in nearly half the test set, which is discordant with their expected guideline-based efficacy. A data-driven personalized statin recommendation approach may inform shared decision making in areas of uncertainty, and highlight unexpected efficacy-effectiveness gaps.

Project description:BackgroundThe recent guidelines for preventing atherosclerotic cardiovascular events are an important advancement. For primary prevention, statins are recommended if the ten-year risk is ≥ 5% (consideration for therapy) or ≥ 7.5% (definitive treatment unless contraindication after discussion). We rationalized that a significant cohort with ten-year risk below the treatment thresholds would predictably surpass them within the recommended 4-6 year window for reassessing the ten-year risk. As atherosclerosis is a progressive disease, these individuals may therefore benefit with more aggressive therapies even at baseline.Methods and findingsWe used publicly available NHANES dataset for ten-year risk calculation. There were 1805 participants. To evaluate the ten-year risk change at five years, we considered two scenarios: no change in the baseline parameters except increased age by five (No Change) and alternatively 10% improvement in systolic BP, total and HDL-c, no smoking with five-year increase in age (Reduced Risk Profile). Amongst non-diabetics with <5% risk at baseline, 35% reached or exceeded 5% risk in five years (5% reached or exceed the 7.5% risk) with No Change and 9% reached or exceeded 5% risk in five years (none reached 7.5% risk) with Reduced Risk Profile; furthermore, 94% of the non-diabetic cohort with baseline risk between 3.5%-5% would exceed the 5% and/or 7.5% boundary limit with No Change. Amongst non-diabetics with 5-7.5% baseline risks, 87% reached or exceeded 7.5% with No Change while 30% reached or exceeded 7.5% risk with Reduced Risk Profile.ConclusionsA significant population cohort at levels below the treatment thresholds will predictably exceed these limits with time with or without improvement in modifiable risk factors and may benefit with more aggressive therapy at baseline. We provide an improved risk calculator that allows for integrating expected risk modification into discussion with an individual. This needs to be prospectively tested in clinical trials.

Dataset Information

MicroRNAs in cardiovascular disease: from pathogenesis to prevention and treatment.

Publications

MicroRNAs in cardiovascular disease: from pathogenesis to prevention and treatment.

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